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How To Pick The Right Artificial Intelligence Consulting Company?

Jul 1, 20205 min read

Miłosz Kaczorowski

Co-founder at Ideamotive. Technological advisor and software consultant.

Machine learning and artificial intelligence are undoubtedly one of the dominant technology trends nowadays. The AI adoption is predicted to skyrocket - up to 80% of all new technologies are going to be AI-based by 2021. The number of companies using artificial intelligence has grown by 270% in the last four years.  

The high adoption rate is no mere the matter of high availability of people behind the technology - a data scientist, data engineer, or a machine learning engineer are both rare and high-paid specialists with a broad employment market and high development perspectives. Also, it is increasingly common for these specialists to be picky when it comes to choosing the employer - so without a good fun room, flexible working hours and access to healthy snacks in the kitchen hiring one can be challenging. 


And a solid bag of cash - didn’t I mention it? Or should I have mentioned it in the first place?


Crisisproof profession?


According to the TowardsDataScience data, the company that mentors and trains multiple data scientists and machine learning-related folk, despite the plunge in the job offers - there is nearly 70% less offers for a data scientist in march that it was in February and far less when compared to March 2019 - no more than 3% of TDS alumni were laid off.  Making the long story short: despite being less willing to hire new ones, companies are far from tossing their machine learning masters. 


Considering that, a company willing to ride the AI train shouldn’t build own data science workforce but find a reliable business partner. But being easier doesn’t mean being easy. 


And that’s why we prepared a guide about picking the right AI consulting company. So what to consider?

Picking the right AI consulting company


From startups to dynamic data science houses to established consulting behemoths, there are multiple companies to choose from when picking up an AI partner. To make things easier one should think about:

Experience and AI knowledge

An obvious one - Artificial Intelligence can be both deeply understood and known only from the surface in the same way it is done with any other type of engineering. The difference can be crucial, with numerous organizations delivering the one-size-fits-them-all type of solution only slightly polished to fit a particular case and others, able to really crack the problem and outthink the challenge. 


On the other way around - the company can both use already developed approaches or build shining new solutions. Both approaches have their pros and cons. There are multiple issues that are solved with already existing techniques available nearly out-of-box and in the IT world, it is nothing to be surprised with. But this type of company probably cannot solve something uncommon and niche. 


On the other side, there is a company composed of top-brainers who can forge super-tech from scratch. But apart from being significantly more expensive, it is not always necessary. But sometimes it is - and that’s where these stars shine. 


AI in Business

Domain experience/knowledge

AI-techniques are domain-agnostic themselves, and that’s cool. There are models that can detect cancer when restrained; they are equally efficient in spotting road signs in an autonomous car system. 


That’s how the model works. But the implementation is a whole different story and the company’s experience can be a game-changer. Every industry has its own established technologies, problems and legal framework to consider when delivering new solutions. A top of mind example - despite a tremendous amount of data gathered and collected by the healthcare industry, it can be challenging to get access to the legally compliant dataset to train the model. 


On the other hand - there can be slight overfitting seen in a highly focused company. The inability to see the challenge in a new way can be blocking and, sometimes, render the problem unsolvable. So the choice can be tricky. 


So when asked about how to choose the right AI consulting partner, one should answer: it depends. Picking a domain expert can result in overfitting to the problem and mental inflexibility while picking an inexperienced partner can result in reinventing the wheel. Either is both a risk or an opportunity to grab.


Python_ The Definitive Business Guide 

Problem-solving approach

Both domain knowledge and AI-related technologies proficiency are nothing when the company lacks a problem-solving approach and the attitude toward delivering an effect. When delivering the artificial intelligence project, there can be multiple situations seen: 

  • There is no problem at all - it is getting increasingly common for companies to invite AI consultants to prepare an audit and check if there is anything to be optimized or boosted with ML-based solutions. The consulting company can see this as an opportunity for an easy earn or a chance to deliver a real improvement. 
  • The problem is unclear and there is no solution - probably the hardest situation when there is a problem to solve and a challenge to overcome, but there is little to no information on how it could be done or what tools there is a need for. There is the best place for the ML-superstars to deliver the show. On the other hand - the ability to crack the problem is not equal with delivering the solution - and that’s what the client is expecting in the end.
  • The problem is known and solutions are available - the most common situation, where the challenge is in tailoring the existing software to the needs of a particular company. In the world of Ai-based business solutions, there is a myriad of technologies to use, so even if a problem is known, it is far from being easy to handle. But it is not a task for ML-superstar, but a well designed IT machine of a professional and agile data science-augmented software house. 

Both the challenge and the approach should be clarified BEFORE the company is picked to avoid overpaying (that’s about you, ML superstars) or mental inability to crack the problem (no finger-pointing). 

Right fit for the company

While the points above were about the competences and qualifications of the business partner, the fit for the company is something from the soft skills kingdom. But it is far from being meaningless. 


The right fit for the company is about working with the same principles, similar approach, and compatible communication style. It can be also about the weight of the player - not every CEO feels comfortable when working with startup freaks and not everyone fits a corporate-style AI consulting Services. 


There are multiple other factors - some people like to be seen as coworking buddies, while others prefer to be a technocratic union with their service provider. 


Any imbalance can cause frustration, with a strong feeling of imbalance (corporate vs startup) or being patronized (buddy vs technocrat). There are multiple other fields of course and the matter is more complicated than it appears. 

Clear value proposition and clarity (vs. AI mumbo-jumbo)

Last but not least, a good AI consulting partner is clear about the offer, value, and terms of cooperation. Artificial intelligence technology is inspiring and attractive. Also, it can deliver significant PR and investor relationship boosts as well as pump up the stock value. 


That’s why it can be tempting for the company to build the image of AI-powered superheroes to solve anything one would wish. But that’s not the point - if there is only a shady promise of a great new business reality instead of a clear value proposition, it is a clear sign of a new technological plague - an AI mumbo-jumbo.


So the key is in seeking a clear value proposition spiced up with a far-sighted approach and the drive toward delivering scalable solutions. 


And that’s not what will make your company shine among others.


Picking up the right AI partner is in fact not that different from choosing any other company to work with - there is a need for knowledge, approach, and cultural fit. The key challenge is in the complex nature of the machine learning technology - even a skilled software developer can feel insecure when facing a mind-boggling world of sophisticated ML algorithms and hermetic slang associated with them. 


On the other hand, you can always think about that in the way you think about any other outsourcing partnership - count the ROI, invest, and reevaluate frequently to keep the cash flowing. But is that so simple when it comes to AI and delivering a future-like 


But fear not. If you have any questions about forging the cooperation with the AI consulting company - here we are, ready to answer them. So don’t hesitate to contact us with the form below!


Miłosz Kaczorowski

Co-Founder of Ideamotive. Highly skilled in Ruby on Rails, JavaScript and Linux System Administration. Experienced in implementing effective web apps.

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